Orbitrap-Based Mass and Charge Analysis of Single Molecules.

Native mass spectrometry is nowadays widely used for determining the mass of intact proteins and their noncovalent biomolecular assemblies. While this technology performs well in the mass determination of monodisperse protein assemblies, more real-life heterogeneous protein complexes can pose a significant challenge. Factors such as co-occurring stoichiometries, subcomplexes, and/or post-translational modifications, may especially hamper mass analysis by obfuscating the charge state inferencing that is fundamental to the technique. Moreover, these mass analyses typically require measurement of several million molecules to generate an analyzable mass spectrum, limiting its sensitivity. In 2012, we introduced an Orbitrap-based mass analyzer with extended mass range (EMR) and demonstrated that it could be used to obtain not only high-resolution mass spectra of large protein macromolecular assemblies, but we also showed that single ions generated from these assemblies provided sufficient image current to induce a measurable charge-related signal. Based on these observations, we and others further optimized the experimental conditions necessary for single ion measurements, which led in 2020 to the introduction of single-molecule Orbitrap-based charge detection mass spectrometry (Orbitrap-based CDMS). The introduction of these single molecule approaches has led to the fruition of various innovative lines of research. For example, tracking the behavior of individual macromolecular ions inside the Orbitrap mass analyzer provides unique, fundamental insights into mechanisms of ion dephasing and demonstrated the (astonishingly high) stability of high mass ions. Such fundamental information will help to further optimize the Orbitrap mass analyzer. As another example, the circumvention of traditional charge state inferencing enables Orbitrap-based CDMS to extract mass information from even extremely heterogeneous proteins and protein assemblies (e.g., glycoprotein assemblies, cargo-containing nanoparticles) via single molecule detection, reaching beyond the capabilities of earlier approaches. We so far demonstrated the power of Orbitrap-based CDMS applied to a variety of fascinating systems, assessing for instance the cargo load of recombinant AAV-based gene delivery vectors, the buildup of immune-complexes involved in complement activation, and quite accurate masses of highly glycosylated proteins, such as the SARS-CoV-2 spike trimer proteins. With such widespread applications, the next objective is to make Orbitrap-based CDMS more mainstream, whereby we still will seek to further advance the boundaries in sensitivity and mass resolving power.

Identification and characterization of quaternary ammonium compounds in Flemish indoor dust by ion-mobility high-resolution mass spectrometry.

Quaternary ammonium compounds (QACs) are a class of surfactants commonly used in disinfecting and cleaning products. Their use has substantially increased during the COVID-19 pandemic leading to increasing human exposure. QACs have been associated with hypersensitivity reactions and an increased risk of asthma. This study introduces the first identification, characterization and semi-quantification of QACs in European indoor dust using ion-mobility high-resolution mass spectrometry (IM-HRMS), including the acquisition of collision cross section values (DTCCSN2) for targeted and suspect QACs. A total of 46 indoor dust samples collected in Belgium were analyzed using target and suspect screening. Targeted QACs (n = 21) were detected with detection frequencies ranging between 4.2 and 100 %, while 15 QACs showed detection frequencies > 90 %. Semi-quantified concentrations of individual QACs showed a maximum of 32.23 µg/g with a median ∑QAC concentration of 13.05 µg/g and allowed the calculation of Estimated Daily Intakes for adults and toddlers. Most abundant QACs matched the patterns reported in indoor dust collected in the United States. Suspect screening allowed the identification of 17 additional QACs. A dialkyl dimethyl ammonium compound with mixed chain lengths (C16:C18) was characterized as a major QAC homologue with a maximum semi-quantified concentration of 24.90 µg/g. The high detection frequencies and structural variabilities observed call for more European studies on potential human exposure to these compounds. For all targeted QACs, drift tube IM-HRMS derived collision cross section values (DTCCSN2) are reported. Reference DTCCSN2 values allowed the characterization of CCS-m/z trendlines for each of the targeted QAC classes. Experimental CCS-m/z ratios of suspect QACs were compared with the CCS-m/z trendlines. The alignment between the two datasets served as an additional confirmation of the assigned suspect QACs. The use of the 4bit multiplexing acquisition mode with consecutive high-resolution demultiplexing confirmed the presence of isomers for two of the suspect QACs.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

Detection of Mutant Peptides of SARS-CoV-2 Variants by LC/MS in the DDA Approach Using an In-House Database.

Equipped with a dramatically high mutation rate, which happens to be a signature of RNA viruses, SARS-CoV-2 trampled across the globe infecting individuals of all ages and ethnicities. As the variants of concern (VOC) loomed large, definitive detection of SARS-CoV-2 strains became a matter of utmost importance in epidemiological and clinical research. Besides, unveiling the disease pathogenesis at the molecular level and deciphering the therapeutic targets became key priorities since the emergence of the pandemic. Mass spectrometry has been largely used in this regard. A critical part of mass spectrometric analyses is the proteome database required for the identification of peptides. Presently, the mutational information on proteins available on SARS-CoV-2 databases cannot be used to analyze data extracted from mass spectrometers. Hence, we developed the novel Mutant Peptide Database (MPD) for the mass spectrometry (MS)-based identification of mutated peptides, which contains information from 11 proteins of SARS-CoV-2 from a total of 21,549 SARS-CoV-2 variants across different regions of India. The database was validated using clinical samples, and its applicability was also demonstrated with the mutated peptides extracted from the literature. We believe that MPD will support broad-spectrum MS-based studies like viral detection, disease pathogenesis, and therapeutics with respect to SARS-CoV-2 and its variants.

Quantitative protein biomarker panels: a path to improved clinical practice through proteomics.

The utilisation of protein biomarker panels, rather than individual protein biomarkers, offers a more comprehensive representation of human physiology. It thus has the potential to improve diagnosis, prognosis and the differentiation of responders from nonresponders in the context of precision medicine. Although several proteomic techniques exist for measuring biomarker panels, the integration of proteomics into clinical practice has been limited. In this Commentary, we highlight the significance of quantitative protein biomarker panels in clinical medicine and outline the challenges that must be addressed in order to identify the most promising panels and implement them in clinical routines to realise their medical potential. Furthermore, we argue that the absolute quantification of protein panels through targeted mass spectrometric assays remains the most promising technology for translating proteomics into routine clinical applications due to its high flexibility, low sample costs, independence from affinity reagents and low entry barriers for its integration into existing laboratory workflows.

Evolution of Mass Spectrometry Instruments and Techniques for Blood Proteomics.

Mass spectrometry (MS)-based blood proteomics is a crucial research focus in identifying disease biomarkers. Blood serum or plasma is the most commonly used sample for such analysis; however, it presents challenges due to the complexity and dynamic range of protein abundance. Despite these difficulties, the development of high-resolution MS instruments has made comprehensive investigation of blood proteomics possible. The evolution of time-of-flight (TOF) or Orbitrap MS instruments has played a significant role in the field of blood proteomics. These instruments are now among the most prominent techniques for blood proteomics due to their sensitivity, selectivity, fast response, and stability. For optimal results, it is necessary to eliminate high-abundance proteins from the blood sample, to maximize the depth coverage of the blood proteomics analysis. This can be achieved through various methods, including commercial kits, chemically synthesized materials, and MS technologies. This paper reviews recent advancements in MS technology and its remarkable applications in biomarker discovery, particularly in the areas of cancer and COVID-19 studies.

An efficient method for qualitation and quantitation of multi-components of the herbal medicine Qingjin Yiqi Granules.

Qingjin Yiqi Granules (QJYQ) is a Traditional Chinese Medicines (TCMs) prescription for the patients with post-COVID-19 condition. It is essential to carry out the quality evaluation of QJYQ. A comprehensive investigation was conducted by establishing deep-learning assisted mass defect filter (deep-learning MDF) mode for qualitative analysis, ultra-high performance liquid chromatography and scheduled multiple reaction monitoring method (UHPLC-sMRM) for precise quantitation to evaluate the quality of QJYQ. Firstly, a deep-learning MDF was used to classify and characterize the whole phytochemical components of QJYQ based on the mass spectrum (MS) data of ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Secondly, the highly sensitive UHPLC-sMRM data-acquisition method was established to quantify the multi-ingredients of QJYQ. Totally, nine major types of phytochemical compounds in QJYQ were intelligently classified and 163 phytochemicals were initially identified. Furthermore, fifty components were rapidly quantified. The comprehensive evaluation strategy established in this study would provide an effective tool for accurately evaluating the quality of QJYQ as a whole.

Evaluation of Workplace Exposures to Volatile Chemicals During COVID-19 Building Disinfection Activities with Proton Transfer Reaction Mass Spectrometry.

We conducted an experimental case study to demonstrate the application of proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) for mobile breathing zone (BZ) monitoring of volatile chemical exposures in workplace environments during COVID-19 disinfection activities. The experiments were conducted in an architectural engineering laboratory-the Purdue zero Energy Design Guidance for Engineers (zEDGE) Tiny House, which served as a simulated workplace environment. Controlled disinfection activities were carried out on impermeable high-touch indoor surfaces, including the entry door, kitchen countertop, toilet bowl, bathroom sink, and shower. Worker inhalation exposure to volatile organic compounds (VOCs) was evaluated by attaching the PTR-TOF-MS sampling line to the researcher's BZ while the disinfection activity was carried out throughout the entire building. The results demonstrate that significant spatiotemporal variations in VOC concentrations can occur in the worker's BZ during multi-surface disinfection events. Application of high-resolution monitoring techniques, such as PTR-TOF-MS, are needed to advance characterization of worker exposures towards the development of appropriate mitigation strategies for volatile disinfectant chemicals.

Liquid Chromatography-Mass Spectrometry-Based Qualitative Profiling of mRNA Therapeutic Reagents Using Stable Isotope-Labeled Standards Followed by the Automatic Quantitation Software Ariadne.

mRNA-based medicines are a promising modality for preventing virus-caused illnesses, including COVID-19, and treating various types of cancer and genetic diseases. To develop such medicines, methods to characterize long mRNA molecules are needed for quality control and metabolic analysis. Here, we developed an analytical platform based on isotope-dilution liquid chromatography-mass spectrometry (LC-MS) that quantitatively characterizes long, modified mRNAs by comparing them to a stable isotope-labeled reference with an identical sequence to that of the target medicine. This platform also includes database searching using the mass spectra as a query, which allowed us to confirm the primary structures of 200 to 4300 nt mRNAs including chemical modifications, with sequence coverage at 100%, to detect/identify defects in the sequences, and to define the efficiencies of the 5'-capping and integrity of the polyadenylated tail. Our findings indicated that this platform should be valuable for quantitatively characterizing mRNA vaccines and other mRNA medicines.

Cov2MS: An Automated and Quantitative Matrix-Independent Assay for Mass Spectrometric Measurement of SARS-CoV-2 Nucleocapsid Protein.

The pandemic readiness toolbox needs to be extended, targeting different biomolecules, using orthogonal experimental set-ups. Here, we build on our Cov-MS effort using LC-MS, adding SISCAPA technology to enrich proteotypic peptides of the SARS-CoV-2 nucleocapsid (N) protein from trypsin-digested patient samples. The Cov2MS assay is compatible with most matrices including nasopharyngeal swabs, saliva, and plasma and has increased sensitivity into the attomole range, a 1000-fold improvement compared to direct detection in a matrix. A strong positive correlation was observed with qPCR detection beyond a quantification cycle of 30-31, the level where no live virus can be cultured. The automatable sample preparation and reduced LC dependency allow analysis of up to 500 samples per day per instrument. Importantly, peptide enrichment allows detection of the N protein in pooled samples without sensitivity loss. Easily multiplexed, we detect variants and propose targets for Influenza A and B detection. Thus, the Cov2MS assay can be adapted to test for many different pathogens in pooled samples, providing longitudinal epidemiological monitoring of large numbers of pathogens within a population as an early warning system.

A Parallelization Strategy for the Time Efficient Analysis of Thousands of LC/MS Runs in High-Performance Computing Environment.

Combining robust proteomics instrumentation with high-throughput enabling liquid chromatography (LC) systems (e.g., timsTOF Pro and the Evosep One system, respectively) enabled mapping the proteomes of 1000s of samples. Fragpipe is one of the few computational protein identification and quantification frameworks that allows for the time-efficient analysis of such large data sets. However, it requires large amounts of computational power and data storage space that leave even state-of-the-art workstations underpowered when it comes to the analysis of proteomics data sets with 1000s of LC mass spectrometry runs. To address this issue, we developed and optimized a Fragpipe-based analysis strategy for a high-performance computing environment and analyzed 3348 plasma samples (6.4 TB) that were longitudinally collected from hospitalized COVID-19 patients under the auspice of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study. Our parallelization strategy reduced the total runtime by ∼90% from 116 (theoretical) days to just 9 days in the high-performance computing environment. All code is open-source and can be deployed in any Simple Linux Utility for Resource Management (SLURM) high-performance computing environment, enabling the analysis of large-scale high-throughput proteomics studies.

Untargeted saliva metabolomics by liquid chromatography-Mass spectrometry reveals markers of COVID-19 severity.

BACKGROUND: The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes-and whether markers can be found in different biofluids-are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. METHODS: Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. RESULTS: Positive percent agreement of 1.00 between a partial least squares-discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. CONCLUSIONS: In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.

A feasibility study of Covid-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometry.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a tremendous threat to global health. polymerase chain reaction (PCR) and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, the food and drug administration (FDA) emergency approved volatile organic component (VOC) as an alternative test for COVID-19 detection. In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by high-pressure photon ionization time-of-flight mass spectrometry for VOCs. Machine learning algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation. The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls. This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection.

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

Molecular characteristics, sources and environmental risk of aromatic compounds in particulate matter during COVID-2019: Nontarget screening by ultra-high resolution mass spectrometry and comprehensive two-dimensional gas chromatography.

Aromatic compounds, including many polycyclic aromatic hydrocarbons (PAHs), are suspected carcinogens and may originate from different sources. To investigate the impact of anthropogenic emission reductions on unknown aromatic compounds in particulate matter, we collected samples during the pre-COVID period in 2020, the COVID-19 lockdown period in 2020, and the same period as the lockdown in 2019. Besides the 16 PAHs, other aromatic compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. Four main compound classes were identified: CH, CHO, CHNO, and CHOS. Hierarchical cluster analysis showed the aromatic compounds varied during the different periods. Compared with before the pandemic, the relative abundances of aromatic compounds with low degrees of unsaturation and long alkyl chains (e.g., alkylbenzenes) increased. These compounds probably mainly arose from fossil fuel combustion and petrochemical industry emissions. The CHO compounds, which were dominated by those with high degrees of oxidation, might originate from secondary organic aerosols. Aromatic aldehydes (e.g., cyclamen aldehyde) and benzoates (e.g., 2-ethylhexyl benzoate) probably with high toxicity deserve more attention. During lockdown, nitro derivatives of condensed PAHs were the main CHNO compounds, and the numbers of homologs decreased perhaps because of significant reductions in NOx and PAHs. CHOS compounds with long carbon chains and low degrees of unsaturation were predominant and the numbers of homologs increased. Five compounds (e.g. 1,3-dimethyl pyrene) were predicted to possibly exhibit persistent and bio-accumulated by EPI Suite model, which need further research. The results provide insight on aromatic compounds and their source appointment in atmospheric particulate matter.

A fast and sensitive absolute quantification assay for the detection of SARS-CoV-2 peptides using parallel reaction monitoring mass spectrometry.

The on-going SARS-CoV-2 (COVID-19) pandemic has called for an urgent need for rapid and high-throughput methods for mass testing and early detection, prevention as well as surveillance of the disease. We investigated whether targeted parallel reaction monitoring (PRM) quantification using high resolution Orbitrap instruments can provide the sensitivity and speed required for a high-throughput method that could be used for clinical diagnosis. We developed a high-throughput and sensitive PRM-MS assay that enables absolute quantification of SARS-CoV-2 nucleocapsid peptides with short turn-around times by using isotopically labelled synthetic SARS-CoV-2 concatenated peptides. We established a fast and high-throughput S-trap-based sample preparation method and utilized it for testing 25 positive and 25 negative heat-inactivated clinical nasopharyngeal swab samples for SARS-CoV-2 detection. The method was able to differentiate between negative and some of the positive patients with high viral load. Moreover, based on the absolute quantification calculations, our data show that patients with Ct values as low as 17.8 correspond to NCAP protein amounts of around 7.5 pmol in swab samples. The present high-throughput method could potentially be utilized in specialized clinics as an alternative tool for detection of SARS-CoV-2 but will require enrichment of viral proteins in order to compete with RT-qPCR.

Site-specific glycosylation of SARS-CoV-2: Big challenges in mass spectrometry analysis.

Glycosylation of viral proteins is required for the progeny formation and infectivity of virtually all viruses. It is increasingly clear that distinct glycans also play pivotal roles in the virus's ability to shield and evade the host's immune system. Recently, there has been a great advancement in structural identification and quantitation of viral glycosylation, especially spike proteins. Given the ongoing pandemic and the high demand for structure analysis of SARS-CoV-2 densely glycosylated spike protein, mass spectrometry methodologies have been employed to accurately determine glycosylation patterns. There are still many challenges in the determination of site-specific glycosylation of SARS-CoV-2 viral spike protein. This is compounded by some conflicting results regarding glycan site occupancy and glycan structural characterization. These are probably due to differences in the expression systems, form of expressed spike glycoprotein, MS methodologies, and analysis software. In this review, we recap the glycosylation of spike protein and compare among various studies. Also, we describe the most recent advancements in glycosylation analysis in greater detail and we explain some misinterpretation of previously observed data in recent publications. Our study provides a comprehensive view of the spike protein glycosylation and highlights the importance of consistent glycosylation determination.

A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics.

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases.

Rapid and precise diagnostic methods are required to control emerging infectious diseases effectively. Human body fluids are attractive clinical samples for discovering diagnostic targets because they reflect the clinical statuses of patients and most of them can be obtained with minimally invasive sampling processes. Body fluids are good reservoirs for infectious parasites, bacteria, and viruses. Therefore, recent clinical proteomics methods have focused on body fluids when aiming to discover human- or pathogen-originated diagnostic markers. Cutting-edge liquid chromatography-mass spectrometry (LC-MS)-based proteomics has been applied in this regard; it is considered one of the most sensitive and specific proteomics approaches. Here, the clinical characteristics of each body fluid, recent tandem mass spectroscopy (MS/MS) data-acquisition methods, and applications of body fluids for proteomics regarding infectious diseases (including the coronavirus disease of 2019 [COVID-19]), are summarized and discussed.

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project.

The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.